| Title: | Toward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification |
| Address: | "Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore. Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117576, Singapore. NUS Suzhou Research Institute (NUSRI), Suzhou, 215123, People's Republic of China. NUS Graduate School for Integrative Science and Engineering (NGS), National University of Singapore, Singapore, 117576, Singapore" |
| ISSN/ISBN: | 1936-086X (Electronic) 1936-0851 (Linking) |
| Abstract: | "As a natural monitor of health conditions for human beings, volatile organic compounds (VOCs) act as significant biomarkers for healthcare monitoring and early stage diagnosis of diseases. Most existing VOC sensors use semiconductors, optics, and electrochemistry, which are only capable of measuring the total concentration of VOCs with slow response, resulting in the lack of selectivity and low efficiency for VOC detection. Infrared (IR) spectroscopy technology provides an effective solution to detect chemical structures of VOC molecules by absorption fingerprints induced by the signature vibration of chemical stretches. However, traditional IR spectroscopy for VOC detection is limited by the weak light-matter interaction, resulting in large optical paths. Leveraging the ultrahigh electric field induced by plasma, the vibration of the molecules is enhanced to improve the light-matter interaction. Herein, we report a plasma-enhanced IR absorption spectroscopy with advantages of fast response, accurate quantization, and good selectivity. An order of approximately kV voltage was achieved from the multiswitched manipulation of the triboelectric nanogenerator by repeated sliding. The VOC species and their concentrations were well-quantified from the wavelength and intensity of spectra signals with the enhancement from plasma. Furthermore, machine learning has visualized the relationship of different VOCs in the mixture, which demonstrated the feasibility of the VOC identification to mimic patients" |
| Keywords: | "Delivery of Health Care Humans Machine Learning Semiconductors Spectrophotometry, Infrared *Volatile Organic Compounds healthcare diagnosis mid-infrared spectroscopy triboelectric nanogenerator volatile organic compound;" |
| Notes: | "MedlineZhu, Jianxiong Ren, Zhihao Lee, Chengkuo eng Research Support, Non-U.S. Gov't 2020/12/15 ACS Nano. 2021 Jan 26; 15(1):894-903. doi: 10.1021/acsnano.0c07464. Epub 2020 Dec 14" |
